Overview

Dataset statistics

Number of variables22
Number of observations21597
Missing cells6754
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory176.0 B

Variable types

DateTime1
Numeric18
Categorical3

Alerts

price is highly overall correlated with sqft_living and 3 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 5 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 3 other fieldsHigh correlation
grade is highly overall correlated with price and 6 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 6 other fieldsHigh correlation
yr_built is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.3%)Imbalance
waterfront has 2391 (11.1%) missing valuesMissing
sqft_basement has 452 (2.1%) missing valuesMissing
yr_renovated has 3848 (17.8%) missing valuesMissing
id is uniformly distributedUniform
id has unique valuesUnique
sqft_basement has 12827 (59.4%) zerosZeros
yr_renovated has 17005 (78.7%) zerosZeros

Reproduction

Analysis started2023-08-09 14:31:42.175642
Analysis finished2023-08-09 14:32:02.334764
Duration20.16 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2023-08-09T16:32:02.379887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:02.522584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3622
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean540296.57
Minimum78000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:02.592906image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum78000
5-th percentile210000
Q1322000
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7622000
Interquartile range (IQR)323000

Descriptive statistics

Standard deviation367368.14
Coefficient of variation (CV)0.67993794
Kurtosis34.541359
Mean540296.57
Median Absolute Deviation (MAD)150000
Skewness4.0233647
Sum1.1668785 × 1010
Variance1.3495935 × 1011
MonotonicityNot monotonic
2023-08-09T16:32:02.655550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 172
 
0.8%
550000 159
 
0.7%
500000 152
 
0.7%
425000 150
 
0.7%
325000 148
 
0.7%
400000 145
 
0.7%
375000 138
 
0.6%
300000 133
 
0.6%
525000 131
 
0.6%
Other values (3612) 20097
93.1%
ValueCountFrequency (%)
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 2
< 0.1%
86500 1
< 0.1%
89000 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7060000 1
< 0.1%
6890000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110000 1
< 0.1%
4670000 1
< 0.1%
4500000 1
< 0.1%
4490000 1
< 0.1%

house_id
Real number (ℝ)

Distinct21420
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5804743 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:02.737485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1274039 × 108
Q12.1230492 × 109
median3.9049304 × 109
Q37.3089005 × 109
95-th percentile9.2973004 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.1858513 × 109

Descriptive statistics

Standard deviation2.8767357 × 109
Coefficient of variation (CV)0.6280432
Kurtosis-1.2607499
Mean4.5804743 × 109
Median Absolute Deviation (MAD)2.4025303 × 109
Skewness0.24322552
Sum9.8924503 × 1013
Variance8.2756084 × 1018
MonotonicityNot monotonic
2023-08-09T16:32:02.805030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795000620 3
 
< 0.1%
8910500150 2
 
< 0.1%
7409700215 2
 
< 0.1%
1995200200 2
 
< 0.1%
9211500620 2
 
< 0.1%
1524079093 2
 
< 0.1%
4305200070 2
 
< 0.1%
1450100390 2
 
< 0.1%
7893805650 2
 
< 0.1%
109200390 2
 
< 0.1%
Other values (21410) 21576
99.9%
ValueCountFrequency (%)
1000102 2
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct21597
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10799
Minimum1
Maximum21597
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:02.867358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1080.8
Q15400
median10799
Q316198
95-th percentile20517.2
Maximum21597
Range21596
Interquartile range (IQR)10798

Descriptive statistics

Standard deviation6234.6612
Coefficient of variation (CV)0.5773369
Kurtosis-1.2
Mean10799
Median Absolute Deviation (MAD)5399
Skewness0
Sum2.33226 × 108
Variance38871000
MonotonicityStrictly increasing
2023-08-09T16:32:02.921549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
14397 1
 
< 0.1%
14405 1
 
< 0.1%
14404 1
 
< 0.1%
14403 1
 
< 0.1%
14402 1
 
< 0.1%
14401 1
 
< 0.1%
14400 1
 
< 0.1%
14399 1
 
< 0.1%
14398 1
 
< 0.1%
Other values (21587) 21587
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
21597 1
< 0.1%
21596 1
< 0.1%
21595 1
< 0.1%
21594 1
< 0.1%
21593 1
< 0.1%
21592 1
< 0.1%
21591 1
< 0.1%
21590 1
< 0.1%
21589 1
< 0.1%
21588 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3732
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:02.965858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range32
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92629889
Coefficient of variation (CV)0.27460539
Kurtosis49.821835
Mean3.3732
Median Absolute Deviation (MAD)1
Skewness2.0236412
Sum72851
Variance0.85802964
MonotonicityNot monotonic
2023-08-09T16:32:03.006584image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.9%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 196
 
0.9%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
1 196
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.9%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
10 3
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.9%
3 9824
45.5%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1158263
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.053811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7689843
Coefficient of variation (CV)0.36344397
Kurtosis1.2793153
Mean2.1158263
Median Absolute Deviation (MAD)0.5
Skewness0.51970928
Sum45695.5
Variance0.59133685
MonotonicityNot monotonic
2023-08-09T16:32:03.100623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2.5 5377
24.9%
1 3851
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1445
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (19) 641
 
3.0%
ValueCountFrequency (%)
0.5 4
 
< 0.1%
0.75 71
 
0.3%
1 3851
17.8%
1.25 9
 
< 0.1%
1.5 1445
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5377
24.9%
2.75 1185
 
5.5%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1034
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2080.3219
Minimum370
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.157079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile940
Q11430
median1910
Q32550
95-th percentile3760
Maximum13540
Range13170
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation918.10613
Coefficient of variation (CV)0.44132889
Kurtosis5.252102
Mean2080.3219
Median Absolute Deviation (MAD)540
Skewness1.4732155
Sum44928711
Variance842918.86
MonotonicityNot monotonic
2023-08-09T16:32:03.215728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1800 129
 
0.6%
1660 129
 
0.6%
1010 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1024) 20302
94.0%
ValueCountFrequency (%)
370 1
< 0.1%
380 1
< 0.1%
390 1
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
470 2
< 0.1%
480 2
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9776
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15099.409
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.275928image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800.8
Q15040
median7618
Q310685
95-th percentile43307.2
Maximum1651359
Range1650839
Interquartile range (IQR)5645

Descriptive statistics

Standard deviation41412.637
Coefficient of variation (CV)2.7426661
Kurtosis285.49581
Mean15099.409
Median Absolute Deviation (MAD)2618
Skewness13.072604
Sum3.2610193 × 108
Variance1.7150065 × 109
MonotonicityNot monotonic
2023-08-09T16:32:03.333714image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 119
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9766) 19803
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4940964
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.380157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53968279
Coefficient of variation (CV)0.36121015
Kurtosis-0.49106576
Mean1.4940964
Median Absolute Deviation (MAD)0.5
Skewness0.61449698
Sum32268
Variance0.29125751
MonotonicityNot monotonic
2023-08-09T16:32:03.425390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10673
49.4%
2 8235
38.1%
1.5 1910
 
8.8%
3 611
 
2.8%
2.5 161
 
0.7%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
1 10673
49.4%
1.5 1910
 
8.8%
2 8235
38.1%
2.5 161
 
0.7%
3 611
 
2.8%
3.5 7
 
< 0.1%
ValueCountFrequency (%)
3.5 7
 
< 0.1%
3 611
 
2.8%
2.5 161
 
0.7%
2 8235
38.1%
1.5 1910
 
8.8%
1 10673
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing2391
Missing (%)11.1%
Memory size168.9 KiB
0.0
19060 
1.0
 
146

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters57618
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19060
88.3%
1.0 146
 
0.7%
(Missing) 2391
 
11.1%

Length

2023-08-09T16:32:03.475975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-09T16:32:03.528634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19060
99.2%
1.0 146
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 38266
66.4%
. 19206
33.3%
1 146
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38412
66.7%
Other Punctuation 19206
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38266
99.6%
1 146
 
0.4%
Other Punctuation
ValueCountFrequency (%)
. 19206
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57618
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38266
66.4%
. 19206
33.3%
1 146
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57618
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38266
66.4%
. 19206
33.3%
1 146
 
0.3%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing63
Missing (%)0.3%
Memory size168.9 KiB
0.0
19422 
2.0
 
957
3.0
 
508
1.0
 
330
4.0
 
317

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters64602
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 19422
89.9%
2.0 957
 
4.4%
3.0 508
 
2.4%
1.0 330
 
1.5%
4.0 317
 
1.5%
(Missing) 63
 
0.3%

Length

2023-08-09T16:32:03.568628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-09T16:32:03.621497image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 19422
90.2%
2.0 957
 
4.4%
3.0 508
 
2.4%
1.0 330
 
1.5%
4.0 317
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 40956
63.4%
. 21534
33.3%
2 957
 
1.5%
3 508
 
0.8%
1 330
 
0.5%
4 317
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43068
66.7%
Other Punctuation 21534
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40956
95.1%
2 957
 
2.2%
3 508
 
1.2%
1 330
 
0.8%
4 317
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 21534
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 64602
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40956
63.4%
. 21534
33.3%
2 957
 
1.5%
3 508
 
0.8%
1 330
 
0.5%
4 317
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64602
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40956
63.4%
. 21534
33.3%
2 957
 
1.5%
3 508
 
0.8%
1 330
 
0.5%
4 317
 
0.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size168.9 KiB
3
14020 
4
5677 
5
1701 
2
 
170
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21597
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Length

2023-08-09T16:32:03.664577image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-09T16:32:03.720530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21597
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14020
64.9%
4 5677
26.3%
5 1701
 
7.9%
2 170
 
0.8%
1 29
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6579155
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.771705image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1731997
Coefficient of variation (CV)0.15320092
Kurtosis1.135148
Mean7.6579155
Median Absolute Deviation (MAD)1
Skewness0.78823664
Sum165388
Variance1.3763975
MonotonicityNot monotonic
2023-08-09T16:32:03.816925image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
7 8974
41.6%
8 6065
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.3%
11 399
 
1.8%
5 242
 
1.1%
12 89
 
0.4%
4 27
 
0.1%
13 13
 
0.1%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 27
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8974
41.6%
8 6065
28.1%
9 2615
 
12.1%
10 1134
 
5.3%
11 399
 
1.8%
12 89
 
0.4%
ValueCountFrequency (%)
13 13
 
0.1%
12 89
 
0.4%
11 399
 
1.8%
10 1134
 
5.3%
9 2615
 
12.1%
8 6065
28.1%
7 8974
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 27
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct942
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1788.5968
Minimum370
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.869379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum370
5-th percentile850
Q11190
median1560
Q32210
95-th percentile3400
Maximum9410
Range9040
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation827.75976
Coefficient of variation (CV)0.4627984
Kurtosis3.4055198
Mean1788.5968
Median Absolute Deviation (MAD)450
Skewness1.4474342
Sum38628326
Variance685186.22
MonotonicityNot monotonic
2023-08-09T16:32:03.922636image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 212
 
1.0%
1010 210
 
1.0%
1200 206
 
1.0%
1220 192
 
0.9%
1140 184
 
0.9%
1400 180
 
0.8%
1060 178
 
0.8%
1180 177
 
0.8%
1340 176
 
0.8%
1250 174
 
0.8%
Other values (932) 19708
91.3%
ValueCountFrequency (%)
370 1
 
< 0.1%
380 1
 
< 0.1%
390 1
 
< 0.1%
410 1
 
< 0.1%
420 2
< 0.1%
430 1
 
< 0.1%
440 1
 
< 0.1%
460 1
 
< 0.1%
470 2
< 0.1%
480 4
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

MISSING  ZEROS 

Distinct303
Distinct (%)1.4%
Missing452
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean291.85722
Minimum0
Maximum4820
Zeros12827
Zeros (%)59.4%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:03.974869image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.49086
Coefficient of variation (CV)1.516121
Kurtosis2.7036047
Mean291.85722
Median Absolute Deviation (MAD)0
Skewness1.5742469
Sum6171321
Variance195798.16
MonotonicityNot monotonic
2023-08-09T16:32:04.040633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12827
59.4%
600 217
 
1.0%
700 209
 
1.0%
500 209
 
1.0%
800 201
 
0.9%
400 184
 
0.9%
1000 148
 
0.7%
900 142
 
0.7%
300 142
 
0.7%
200 105
 
0.5%
Other values (293) 6761
31.3%
(Missing) 452
 
2.1%
ValueCountFrequency (%)
0 12827
59.4%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 6
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.9997
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.097357image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11951
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)46

Descriptive statistics

Standard deviation29.375234
Coefficient of variation (CV)0.014903723
Kurtosis-0.65769443
Mean1970.9997
Median Absolute Deviation (MAD)23
Skewness-0.46944998
Sum42567680
Variance862.90438
MonotonicityNot monotonic
2023-08-09T16:32:04.156028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 453
 
2.1%
2005 450
 
2.1%
2004 433
 
2.0%
2003 420
 
1.9%
2007 417
 
1.9%
1977 417
 
1.9%
1978 387
 
1.8%
1968 381
 
1.8%
2008 367
 
1.7%
Other values (106) 17313
80.2%
ValueCountFrequency (%)
1900 87
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 46
0.2%
1904 45
0.2%
1905 74
0.3%
1906 92
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 201
 
0.9%
2012 170
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 230
1.1%
2008 367
1.7%
2007 417
1.9%
2006 453
2.1%

yr_renovated
Real number (ℝ)

MISSING  ZEROS 

Distinct70
Distinct (%)0.4%
Missing3848
Missing (%)17.8%
Infinite0
Infinite (%)0.0%
Mean836.65052
Minimum0
Maximum20150
Zeros17005
Zeros (%)78.7%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.216108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum20150
Range20150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4000.1106
Coefficient of variation (CV)4.7811009
Kurtosis18.911492
Mean836.65052
Median Absolute Deviation (MAD)0
Skewness4.572505
Sum14849710
Variance16000884
MonotonicityNot monotonic
2023-08-09T16:32:04.274043image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 17005
78.7%
20140 73
 
0.3%
20130 31
 
0.1%
20030 31
 
0.1%
20070 30
 
0.1%
20000 29
 
0.1%
20050 29
 
0.1%
20040 22
 
0.1%
19900 22
 
0.1%
20090 21
 
0.1%
Other values (60) 456
 
2.1%
(Missing) 3848
 
17.8%
ValueCountFrequency (%)
0 17005
78.7%
19340 1
 
< 0.1%
19400 2
 
< 0.1%
19440 1
 
< 0.1%
19450 3
 
< 0.1%
19460 1
 
< 0.1%
19480 1
 
< 0.1%
19500 1
 
< 0.1%
19510 1
 
< 0.1%
19530 1
 
< 0.1%
ValueCountFrequency (%)
20150 14
 
0.1%
20140 73
0.3%
20130 31
0.1%
20120 8
 
< 0.1%
20110 9
 
< 0.1%
20100 15
 
0.1%
20090 21
 
0.1%
20080 15
 
0.1%
20070 30
0.1%
20060 20
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.952
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.335829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.513072
Coefficient of variation (CV)0.00054561776
Kurtosis-0.85400486
Mean98077.952
Median Absolute Deviation (MAD)42
Skewness0.40532219
Sum2.1181895 × 109
Variance2863.6489
MonotonicityNot monotonic
2023-08-09T16:32:04.396870image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 589
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 547
 
2.5%
98034 545
 
2.5%
98118 507
 
2.3%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16100
74.5%
ValueCountFrequency (%)
98001 361
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5033
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560093
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.459086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.5718
Q347.678
95-th percentile47.7497
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.13855177
Coefficient of variation (CV)0.0029131938
Kurtosis-0.67579021
Mean47.560093
Median Absolute Deviation (MAD)0.1049
Skewness-0.48552159
Sum1027155.3
Variance0.019196592
MonotonicityNot monotonic
2023-08-09T16:32:04.516312image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6846 17
 
0.1%
47.5322 17
 
0.1%
47.6624 17
 
0.1%
47.6711 16
 
0.1%
47.6955 16
 
0.1%
47.6886 16
 
0.1%
47.6647 15
 
0.1%
47.6904 15
 
0.1%
47.686 15
 
0.1%
Other values (5023) 21436
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.21398
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21597
Negative (%)100.0%
Memory size168.9 KiB
2023-08-09T16:32:04.573631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.231
Q3-122.125
95-th percentile-121.9798
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14072352
Coefficient of variation (CV)-0.0011514518
Kurtosis1.052123
Mean-122.21398
Median Absolute Deviation (MAD)0.101
Skewness0.88488948
Sum-2639455.4
Variance0.019803108
MonotonicityNot monotonic
2023-08-09T16:32:04.634032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 115
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20586
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.6203
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.693345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.23047
Coefficient of variation (CV)0.34492271
Kurtosis1.5917328
Mean1986.6203
Median Absolute Deviation (MAD)410
Skewness1.1068754
Sum42905039
Variance469540.8
MonotonicityNot monotonic
2023-08-09T16:32:04.744787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 180
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1720 166
 
0.8%
1800 166
 
0.8%
1620 164
 
0.8%
Other values (767) 19835
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8682
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12758.284
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size168.9 KiB
2023-08-09T16:32:04.800369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile2002.4
Q15100
median7620
Q310083
95-th percentile37045.2
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27274.442
Coefficient of variation (CV)2.137783
Kurtosis151.39566
Mean12758.284
Median Absolute Deviation (MAD)2505
Skewness9.524362
Sum2.7554065 × 108
Variance7.4389518 × 108
MonotonicityNot monotonic
2023-08-09T16:32:04.852033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 356
 
1.6%
6000 288
 
1.3%
7200 210
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8672) 19582
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2023-08-09T16:32:00.813698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.201365image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.424820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.663694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.665024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.599088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.715634image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.717878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.704761image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.933823image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.815867image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.752173image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.678571image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.677609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.935638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.939539image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.919570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.850013image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.871901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.268108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.488605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.719326image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.717898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.656004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.771563image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.799394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.759922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.989120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.866748image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.807008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.732074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.735702image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.001578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.994918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.974888image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.905085image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.920692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.340077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.550900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.768999image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.768124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.707712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.823609image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.862575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.819886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.042059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.915934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.855406image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.785149image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.016160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.056864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.052530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.034807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.954060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.968690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.435643image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.601259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.846633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.817918image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.759260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.872835image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.911605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.868781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.085791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.963252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.907458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.831646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.075768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.108764image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.101486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.084455image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.002475image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.018017image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.517550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.657887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.898860image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.868922image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.813644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.923965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.962518image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.924817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.134061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.011557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.958494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.882671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.128790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.164477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.154499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.134543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.058319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.074296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.602068image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.712201image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.952985image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.920614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.038760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.976610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.015030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.981319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.181298image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.063105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.009134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.939854image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.182447image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.224334image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.207088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.186713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.115965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.382003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.677282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.767532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.007166image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.971724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.091278image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.027131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.068552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.039456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.231902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.114320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.062319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.028495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.237632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.276961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.261488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.237606image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.170503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.435717image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.739148image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.101014image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.055338image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.021112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.143399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.084420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.121160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.099575image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.282037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.164947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.112924image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.106733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.301220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.330955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.316061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.288885image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.220976image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.483435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.795583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.152582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.103712image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.069126image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.191686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.136849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.177239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.151032image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.330393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.213994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.166096image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.156492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.354458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.390112image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.368102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.338088image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.272808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.527732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.843117image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.202814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.147573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.119051image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.241467image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.184438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.224124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.201465image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.375391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.260040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.213105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.206324image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.404819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.443273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.414141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.389145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.320487image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.572143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.893495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.248797image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.210028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.168296image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.291154image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.265323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.274061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.248742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.425290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.310486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.258517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.252926image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.462828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.490730image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.466042image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.437194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.368812image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.621688image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:43.945356image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.297700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.282905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.217450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.342064image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.330262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.325236image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.305351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.471420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.390286image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.309275image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.305525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.523811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.544215image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.514485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.487276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.415369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.670684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.008709image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.349097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.333482image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.274322image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.395531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.386601image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.376585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.555431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.520756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.448986image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.369181image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.358827image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.575492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.607323image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.567339image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.537528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.509673image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.721698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.089008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.406559image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.383733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.335320image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.453082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.440285image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.433024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.609248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.571525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.500809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.423309image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.419592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.636179image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.674413image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.620820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.591689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.564611image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.776461image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.191987image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.460806image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.437401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.389653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.506690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.496319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.491056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.672082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.622115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.552370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.475825image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.475708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.691000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.729828image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.682886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.648012image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.619756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.830442image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.259040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.514438image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.486411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.446328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.558796image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.555060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.542767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.763589image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.669231image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.607675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.533829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.529603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.747348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.785778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.732564image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.698340image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.670832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.878554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.314409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.567736image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.553858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.496789image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.615614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.608099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.598289image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.836116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.720800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.656790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.583965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.584561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.801481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.837031image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.798093image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.754062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.719886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:01.931704image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:44.368652image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:45.618400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:46.614785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:47.551267image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:48.665713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:49.664886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:50.654054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:51.887246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:52.770653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:53.703252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:54.631884image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:55.631771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:56.865213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:57.890629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:58.871425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:31:59.803023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-08-09T16:32:00.765491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-08-09T16:32:04.914374image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
pricehouse_ididbedroomsbathroomssqft_livingsqft_lotfloorsgradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15waterfrontviewcondition
price1.0000.0040.0400.3440.4970.6440.0750.3220.6580.5420.2520.1020.106-0.0090.4560.0640.5720.0630.3330.2070.023
house_id0.0041.0000.0050.0060.0150.002-0.1170.0190.0200.0040.0020.027-0.013-0.005-0.0040.0070.000-0.1150.0060.0290.030
id0.0400.0051.0000.0120.1130.044-0.1350.1710.0940.069-0.0340.229-0.0230.000-0.0040.0010.019-0.1300.0150.0170.111
bedrooms0.3440.0060.0121.0000.5210.6480.2170.2280.3800.5400.2300.1810.014-0.168-0.0220.1930.4440.2020.0160.0380.012
bathrooms0.4970.0150.1130.5211.0000.7460.0690.5480.6580.6910.1910.5680.042-0.2050.0080.2620.5710.0640.1090.1110.122
sqft_living0.6440.0020.0440.6480.7461.0000.3050.4010.7160.8430.3280.3530.053-0.2070.0310.2850.7470.2850.1510.1470.056
sqft_lot0.075-0.117-0.1350.2170.0690.3051.000-0.2340.1520.2730.036-0.0370.005-0.319-0.1220.3710.3600.9220.0220.0410.040
floors0.3220.0190.1710.2280.5480.401-0.2341.0000.5020.599-0.2720.5510.009-0.0620.0240.1490.306-0.2310.0170.0220.178
grade0.6580.0200.0940.3800.6580.7160.1520.5021.0000.7120.0930.5010.018-0.1820.1040.2230.6630.1570.1290.1420.128
sqft_above0.5420.0040.0690.5400.6910.8430.2730.5990.7121.000-0.1660.4720.029-0.279-0.0260.3860.6970.2550.0840.0890.106
sqft_basement0.2520.002-0.0340.2300.1910.3280.036-0.2720.093-0.1661.000-0.1780.0650.1150.116-0.2010.1300.0300.1460.1590.092
yr_built0.1020.0270.2290.1810.5680.353-0.0370.5510.5010.472-0.1781.000-0.216-0.317-0.1260.4130.336-0.0160.0350.0420.248
yr_renovated0.106-0.013-0.0230.0140.0420.0530.0050.0090.0180.0290.065-0.2161.0000.0680.028-0.080-0.0040.0050.0850.1060.069
zipcode-0.009-0.0050.000-0.168-0.205-0.207-0.319-0.062-0.182-0.2790.115-0.3170.0681.0000.249-0.577-0.287-0.3260.0790.0740.074
lat0.456-0.004-0.004-0.0220.0080.031-0.1220.0240.104-0.0260.116-0.1260.0280.2491.000-0.1430.027-0.1160.0330.0680.057
long0.0640.0070.0010.1930.2620.2850.3710.1490.2230.386-0.2010.413-0.080-0.577-0.1431.0000.3810.3730.0920.0850.081
sqft_living150.5720.0000.0190.4440.5710.7470.3600.3060.6630.6970.1300.336-0.004-0.2870.0270.3811.0000.3660.0920.1470.062
sqft_lot150.063-0.115-0.1300.2020.0640.2850.922-0.2310.1570.2550.030-0.0160.005-0.326-0.1160.3730.3661.0000.0000.0350.013
waterfront0.3330.0060.0150.0160.1090.1510.0220.0170.1290.0840.1460.0350.0850.0790.0330.0920.0920.0001.0000.6000.019
view0.2070.0290.0170.0380.1110.1470.0410.0220.1420.0890.1590.0420.1060.0740.0680.0850.1470.0350.6001.0000.025
condition0.0230.0300.1110.0120.1220.0560.0400.1780.1280.1060.0920.2480.0690.0740.0570.0810.0620.0130.0190.0251.000

Missing values

2023-08-09T16:32:02.011738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-09T16:32:02.161596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-09T16:32:02.293272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

datepricehouse_ididbedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
02014-10-13221900.000712930052013.0001.0001180.0005650.0001.000NaN0.000371180.0000.00019550.0009817847.511-122.2571340.0005650.000
12014-12-09538000.000641410019223.0002.2502570.0007242.0002.0000.0000.000372170.000400.000195119910.0009812547.721-122.3191690.0007639.000
22015-02-25180000.000563150040032.0001.000770.00010000.0001.0000.0000.00036770.0000.0001933NaN9802847.738-122.2332720.0008062.000
32014-12-09604000.000248720087544.0003.0001960.0005000.0001.0000.0000.000571050.000910.00019650.0009813647.521-122.3931360.0005000.000
42015-02-18510000.000195440051053.0002.0001680.0008080.0001.0000.0000.000381680.0000.00019870.0009807447.617-122.0451800.0007503.000
52014-05-121230000.000723755031064.0004.5005420.000101930.0001.0000.0000.0003113890.0001530.00020010.0009805347.656-122.0054760.000101930.000
62014-06-27257500.000132140006073.0002.2501715.0006819.0002.0000.0000.000371715.000NaN19950.0009800347.310-122.3272238.0006819.000
72015-01-15291850.000200800027083.0001.5001060.0009711.0001.0000.000NaN371060.0000.00019630.0009819847.410-122.3151650.0009711.000
82015-04-15229500.000241460012693.0001.0001780.0007470.0001.0000.0000.000371050.000730.00019600.0009814647.512-122.3371780.0008113.000
92015-03-12323000.0003793500160103.0002.5001890.0006560.0002.0000.0000.000371890.0000.00020030.0009803847.368-122.0312390.0007570.000
datepricehouse_ididbedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
215872014-08-25507250.0007852140040215883.0002.5002270.0005536.0002.000NaN0.000382270.0000.00020030.0009806547.539-121.8812270.0005731.000
215882015-01-26429000.0009834201367215893.0002.0001490.0001126.0003.0000.0000.000381490.0000.00020140.0009814447.570-122.2881400.0001230.000
215892014-10-14610685.0003448900210215904.0002.5002520.0006023.0002.0000.000NaN392520.0000.00020140.0009805647.514-122.1672520.0006023.000
215902015-03-261010000.0007936000429215914.0003.5003510.0007200.0002.0000.0000.000392600.000910.00020090.0009813647.554-122.3982050.0006200.000
215912015-02-19475000.0002997800021215923.0002.5001310.0001294.0002.0000.0000.000381180.000130.00020080.0009811647.577-122.4091330.0001265.000
215922014-05-21360000.000263000018215933.0002.5001530.0001131.0003.0000.0000.000381530.0000.00020090.0009810347.699-122.3461530.0001509.000
215932015-02-23400000.0006600060120215944.0002.5002310.0005813.0002.0000.0000.000382310.0000.00020140.0009814647.511-122.3621830.0007200.000
215942014-06-23402101.0001523300141215952.0000.7501020.0001350.0002.0000.0000.000371020.0000.00020090.0009814447.594-122.2991020.0002007.000
215952015-01-16400000.000291310100215963.0002.5001600.0002388.0002.000NaN0.000381600.0000.00020040.0009802747.535-122.0691410.0001287.000
215962014-10-15325000.0001523300157215972.0000.7501020.0001076.0002.0000.0000.000371020.0000.00020080.0009814447.594-122.2991020.0001357.000